In order to solve the problem that the liquid level measuring mechanism of ultra-low temperature liquid such as LNG is too complicated, a design method of sensor heat insulation mechanism is proposed, so that the instrument can complete the non-contact detection of ultra-low temperature liquid under normal temperature environment. The thermal insulation mechanism is determined according to the measurement process, the heat transfer model of the measurement channel is established, the control structure of the thermal insulation channel is determined, and the functional relationship between its size and heat transfer is established. Finally, the dimensional correlation relationship under the minimum heat transfer is obtained by extreme value optimization. In the typical ultra-low temperature liquid level measurement conditions, the actual structure size and temperature control effect were calculated and verified. Through the comparison with the finite element simulation structure of the structure, it was proved that the proposed thermal insulation mechanism can meet the temperature isolation and heat dissipation control requirements of the ultra-low temperature liquid level.
Aiming at the poor application of diagnosis of infant neuromotor diseases such as cerebral palsy based on computer vison, an OpenPose-based detection model for infant cerebral palsy by extracting features from infant spontaneous motion is proposed. Firstly, the deep separable convolution and the residual network structure is used to reduce the degradation of network operation and improve the detection accuracy of joint points. Then, it gives a loss function model based on Smooth L1 to improve the detection accuracy of infant motion features. Finally, motion characteristics are assigned to support vector machine to classify the infant cerebral palsy, which realizes pre-diagnosis with several features. Experiments conducted on dataset show the accuracy of this proposed method is 7~8% higher than others and reduce the amount of calculation to 1/9 of the original and accuracy of prediction can reach 91.89%. The results show that the detection model is feasible and effective for on-line pre-diagnosis of infant cerebral palsy.
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